Abstract
Conditional sequence generation aims to instruct the generation procedure by conditioning the model with additional context information, which is an interesting research issue in AI and machine learning. Unfortunately, current state-of-the-art generative models for music fail to generate good melodies due to the discrete-valued property of music attributes. In this paper, we propose a novel conditional hybrid GAN (C-Hybrid-GAN) for melody generation from lyrics. Three discrete sequences corresponding to music attributes, namely pitch, duration, and rest, are separately generated by melody generation model conditioned on the same lyrics. Gumbel-Softmax is used to approximate the distribution of discrete-valued samples so as to directly generate discrete melody attributes. Most importantly, a hybrid structure is proposed, which contains three independent branches (each for one melody attribute) in the generator and one branch for distinguishing concatenated attributes in the discriminator. Relational memory core is exploited to model not only the dependency inside each sequence of attribute during the training of the generator, but also the consistency among three sequences of attributes during the training of the discriminator. Through extensive experiments using evaluation metrics, e.g., maximum mean discrepancy, average rest value, and MIDI number transition, we demonstrate that the proposed C-Hybrid-GAN outperforms the existing methods in melody generation from lyrics.
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The datasets generated during and/or analyzed during the current study are available in [1] repository, https://github.com/yy1lab/Lyrics-Conditioned-Neural-Melody-Generation.
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Yu, Y., Zhang, Z., Duan, W. et al. Conditional hybrid GAN for melody generation from lyrics. Neural Comput & Applic 35, 3191–3202 (2023). https://doi.org/10.1007/s00521-022-07863-5
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DOI: https://doi.org/10.1007/s00521-022-07863-5